class_docstr: Lexicon acquisition. produce opinion lexicon and an aspect lexicon based
  on input dataset.
  
  Attributes:
      opinion_candidate_list_curr_iter (dict): candidate opinion terms in the current iteration
      opinion_candidate_list_prev_iter (dict): opinion candidates list of previous iteration
      opinion_candidate_list (dict): opinion terms learned across all iterations
      opinion_candidates_list_final (list): final opinion candidates list
      opinion_candidate_list_raw (dict): all instances of candidate opinion terms
                                         across all iterations
      aspect_candidate_list_curr_iter (dict): candidate terms in the current iteration
      aspects_candidate_list_prev_iter(list): Aspect candidates list of previous iteration
      aspect_candidate_list (list):  aspect terms learned across all iterations
      aspect_candidates_list_final (list): final aspect candidates list
      aspect_candidate_list_raw (dict): all instances of candidate aspect terms
                                        across all iterations
docstr: Extract candidate terms from parsed document iterator.
  
  Args:
      parsed_document_iter (Iterator): Parsed document iterator.
      num_of_docs (int): number of documents on iterator.
docstr_tok: ["Extract","candidate","terms","from","parsed","document","iterator",".","Args",":","parsed_document_iter","(","Iterator",")",":","Parsed","document","iterator",".","num_of_docs","(","int",")",":","number","of","documents","on","iterator","."]
code: def extract_opinion_and_aspect_terms(
      self, parsed_document_iter, num_of_docs
  ):
      """Extract candidate terms from parsed document iterator.
  
      Args:
          parsed_document_iter (Iterator): Parsed document iterator.
          num_of_docs (int): number of documents on iterator.
      """
  
      for parsed_document in tqdm(
          parsed_document_iter, total=num_of_docs, file=sys.stdout
      ):
          self.extract_terms_from_doc(parsed_document)
  
imports: import copy
  import re
  import sys
  from os import PathLike
  from tqdm import tqdm
  from nlp_architect.models.absa import TRAIN_LEXICONS, LEXICONS_OUT
  from nlp_architect.models.absa import GENERIC_OP_LEX
  from nlp_architect.models.absa.inference.data_types import Polarity
  from nlp_architect.models.absa.train.data_types import AspectTerm, DepRelation, DepRelationTerm, LoadOpinionStopLists, LoadAspectStopLists, OpinionTerm, QualifiedTerm
  from nlp_architect.models.absa.train.rules import rule_1, rule_2, rule_3, rule_4, rule_5, rule_6
  from nlp_architect.models.absa.utils import _load_parsed_docs_from_dir, _write_final_opinion_lex, _load_lex_as_list_from_csv, read_generic_lex_from_file
label_desc: Use image node_id 3 for calling the AcquireTerms obj's underlying member method code with example usage: obj.extract_opinion_and_aspect_terms(parsed_document_iter, num_of_docs) without return types